Blind phase/frequency synchronization with low-precision ADC: a Bayesian approach
|
|
- Thomas Ray
- 5 years ago
- Views:
Transcription
1 Blind phase/frequency synchronization with low-precision ADC: a Bayesian approach Aseem Wadhwa, Upamanyu Madhow Department of ECE, UCSB 1/26
2 Modern Communication Receiver Architecture Analog Digital TX Channel Analog Preprocessing (Downconversion, AGC etc) ADC DSP Leveraging Moore s Law Faithful Conversion Synchronization Equalization Decoding (low quantization error) 2/26
3 Challenge Wideband (multi GHz) Applications: mm-wave communication (eg. 60 GHz) Backplane chip-to-chip communication Optical Fiber communication Effective Number of Bits (ENOB) f s (Hz) - sampling frequency * Data from B. Murmann, "ADC Performance Survey ," [Online]. Available: 3/26
4 Low Precision ADC Are DSP-centric designs with heavily quantized samples effective? Shannon-theoretic analysis : loss in channel capacity relatively small (J. Singh et al 2009) AWGN channel Block Non-coherent Communication 4/26
5 Revisiting DSP Algorithms with significant Non-linearity Reconsider classical problems: synchronization, equalization etc Architecture: Bayesian Mixed-Signal Processing TX Channel Analog Frontend (Preprocessing) A-to-D Conversion Coarse Digitally Controlled Feedback (AGC, phase rotation etc) Bayesian Inference (DSP) Non-linear Algorithms 5/26
6 Focus of our paper Canonical Problem of Blind phase/frequency synchronization TX complex symbols (unknown) Unknown Channel Phase Unknown Frequency Offset Complex AWGN Observations: Heavily Quantized Phase Simplifying Assumptions: Non-Dispersive Channel Perfect Timing Sync, Nyquist rate symbols differentially modulated QPSK 6/26
7 Outline 1. Receiver Architecture 2. Rapid Phase Acquisition (Blind mode) 3. Phase/Frequency Tracking (Decision Directed mode) 7/26
8 Receiver Architecture: Phase-only Quantization using 1-bit ADCs (AGC-free quantization) Received Passband Waveform Downconversion I Q Pass Linear combinations of I & Q through 1-bit ADCs M=8 bins (4 ADCs) M=12 bins (6 ADCs) 8/26
9 Receiver Architecture: Feedback Transmitter Phase Quantization Channel QPSK Feedback DSP for Bayesian estimation Decoded Symbols Quantized phase measurements (1,2,..,M) Digitally controlled Derotation Phase 9/26
10 Phase Acquisition example: T s = (6 GHz) -1 f c = 60GHz Δf = (100)10-6 * f c Set Δf=0 consider unquantized phase u {1,3,5,7} Blind Mode (Unknown Sequence) net phase rotation 10/26
11 (1) Bayesian Estimation (Given θ k ) Conditional Distribution of u has a closed form expression (2) M=12 bins (6 ADCs) 11/26
12 (3) Recursive Bayes Update Updated Posterior Old Posterior Step Update How to set the derotation phase? 12/26
13 Constant θ k : Issues Example 1 : 4 ADCs (M=8 bins) Bimodal Posterior with Peaks at x 0 and (45 x) 0 θ k = 0 0 θ k = 10 0 θ k Random Posterior of φ after 150 symbols (SNR = 5dB) 13/26
14 Constant θ k : Issues Example 2 : High SNR θ k = 0 0 θ k = 10 0 θ k Random Posterior of φ after 30 symbols (SNR = 35dB) 1. Dithering is Required 2. Random Dithering is a robust choice 14/26
15 Can we do better than Random Dither? Optimizing {θ k } to minimize MSE difficult problem - leads to Partially Observable Markov Decision Process Problem (POMDP) We propose an Information-theoretic Greedy Entropy Policy Idea : Minimize the entropy of the posterior distribution across choices of derotation phase over the next step 15/26
16 Greedy Entropy Policy (Single Horizon) Latest Posterior : Next Posterior : average over f k-1 (φ) 16/26
17 Greedy Entropy Policy: What it tries to do? Roughly, it tries to moves the net phase, prior to quantization, towards boundary at high SNR middle for M=12 at low SNR boundary for M=8 at low SNR Ē(θ) becomes flatter as variance of posterior of φ (f(φ)) decreases --> Random Dithering initially, when variance is high but estimate is good, better than randomly choosing θ M=8 M=12 17/26
18 Simulation Results (1/4) Low SNR (5dB) 8 bins Expected RMSE Pr(error) < /26
19 Simulation Results (2/4) Low SNR (5dB) 12 bins Expected RMSE Pr(error) < /26
20 Simulation Results (3/4) High SNR (15dB) 8 bins Expected RMSE Pr(error) < /26
21 Simulation Results (4/4) High SNR (15dB) 12 bins Expected RMSE Pr(error) < 5 0 High SNR and coarse quantization Gains by optimizing Dither 21/26
22 Frequency Estimation example: T s = (6 GHz) -1 f c = 60GHz Δf = (100)10-6 * f c Phase almost constant for few 10s of symbols This motivates a Hierarchical Approach: 1. windowed MAP estimate of phase coarse estimate 2. feed to an EKF for tracking both phase and frequency 22/26
23 Extended Kalman Filter (EKF) State model Process Model Measurement Model 23/26
24 Remarks Derotation Phases: set to current estimate of phase undo channel phase Decision Directed Mode W=50, Tracking Robust to choice of R (R = [0.1 0, 0 0.1] T ) Q (process noise) controls speed of adaptation and accuracy of estimates 24/26
25 Simulations 25/26
26 Conclusions Digitally controlled Analog pre-processing provides dither Bayesian algorithms for estimation and feedback generation Future Work: Modeling other non-idealities (fading, dispersion, timing asynchronism) Larger Constellations Fundamental limits on performance 26/26
27 Back up Slides 27/26
28 *Walden /26
29 uniform action Example Φ = 10 0 θ = net phase posterior φ posterior Φ = 10 0 θ = net phase posterior φ posterior 29/26
30 30/26
31 31/26
32 32/26
Blind phase/frequency synchronization with low-precision ADC: a Bayesian approach
Blind phase/frequency synchronization with low-precision ADC: a Bayesian approach Aseem Wadhwa and Upamanyu Madhow Department of ECE, University of California Santa Barbara, CA 936 Email: {aseem, madhow}@ece.ucsb.edu
More information4432 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 64, NO. 17, SEPTEMBER 1, Aseem Wadhwa and Upamanyu Madhow
443 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 64, NO. 17, SEPTEMBER 1, 016 Near-Coherent QPSK Performance With Coarse Phase Quantization: A Feedback-Based Architecture for Joint Phase/Frequency Synchronization
More informationScalable Front End Designs for Communication and Learning. Aseem Wadhwa, Department of ECE UCSB PhD Defense
Scalable Front End Designs for Communication and Learning Aseem Wadhwa, Department of ECE UCSB PhD Defense 1 Estimation/Detection Problem Artificial/Natural distortions Receiver eg: Communication System,
More informationScalable Front End Designs for Communication and Learning
UNIVERSITY OF CALIFORNIA Santa Barbara Scalable Front End Designs for Communication and Learning A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy
More informationChannel Estimation with Low-Precision Analog-to-Digital Conversion
Channel Estimation with Low-Precision Analog-to-Digital Conversion Onkar Dabeer School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai India Email: onkar@tcs.tifr.res.in
More informationSpace-time Slicer Architectures for Analog-to-Information Conversion in Channel Equalizers
Space-time Slicer Architectures for Analog-to-Information Conversion in Channel Equalizers Aseem Wadhwa, Upamanyu Madhow and Naresh Shanbhag Department of ECE, University of California Santa Barbara, CA
More informationCapacity of the Discrete-Time AWGN Channel Under Output Quantization
Capacity of the Discrete-Time AWGN Channel Under Output Quantization Jaspreet Singh, Onkar Dabeer and Upamanyu Madhow Abstract We investigate the limits of communication over the discrete-time Additive
More informationOn the Limits of Communication with Low-Precision Analog-to-Digital Conversion at the Receiver
1 On the Limits of Communication with Low-Precision Analog-to-Digital Conversion at the Receiver Jaspreet Singh, Onkar Dabeer, and Upamanyu Madhow, Abstract As communication systems scale up in speed and
More informationCommunication Limits with Low Precision Analog-to-Digital Conversion at the Receiver
This full tet paper was peer reviewed at the direction of IEEE Communications Society subject matter eperts for publication in the ICC 7 proceedings. Communication Limits with Low Precision Analog-to-Digital
More informationAnalysis of Receiver Quantization in Wireless Communication Systems
Analysis of Receiver Quantization in Wireless Communication Systems Theory and Implementation Gareth B. Middleton Committee: Dr. Behnaam Aazhang Dr. Ashutosh Sabharwal Dr. Joseph Cavallaro 18 April 2007
More informationDigital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10
Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,
More informationDynamic Multipath Estimation by Sequential Monte Carlo Methods
Dynamic Multipath Estimation by Sequential Monte Carlo Methods M. Lentmaier, B. Krach, P. Robertson, and T. Thiasiriphet German Aerospace Center (DLR) Slide 1 Outline Multipath problem and signal model
More informationTiming Recovery at Low SNR Cramer-Rao bound, and outperforming the PLL
T F T I G E O R G A I N S T I T U T E O H E O F E A L P R O G R ESS S A N D 1 8 8 5 S E R V L O G Y I C E E C H N O Timing Recovery at Low SNR Cramer-Rao bound, and outperforming the PLL Aravind R. Nayak
More informationAnalog Electronics 2 ICS905
Analog Electronics 2 ICS905 G. Rodriguez-Guisantes Dépt. COMELEC http://perso.telecom-paristech.fr/ rodrigez/ens/cycle_master/ November 2016 2/ 67 Schedule Radio channel characteristics ; Analysis and
More informationLow Resolution Adaptive Compressed Sensing for mmwave MIMO receivers
Low Resolution Adaptive Compressed Sensing for mmwave MIMO receivers Cristian Rusu, Nuria González-Prelcic and Robert W. Heath Motivation 2 Power consumption at mmwave in a MIMO receiver Power at 60 GHz
More informationConstellation Shaping for Communication Channels with Quantized Outputs
Constellation Shaping for Communication Channels with Quantized Outputs, Dr. Matthew C. Valenti and Xingyu Xiang Lane Department of Computer Science and Electrical Engineering West Virginia University
More informationModulation Diversity in Fading Channels with Quantized Receiver
011 IEEE International Symposium on Information Theory Proceedings Modulation Diversity in Fading Channels with Quantized Receiver Saif Khan Mohammed, Emanuele Viterbo, Yi Hong, and Ananthanarayanan Chockalingam
More informationLecture 2: From Linear Regression to Kalman Filter and Beyond
Lecture 2: From Linear Regression to Kalman Filter and Beyond Department of Biomedical Engineering and Computational Science Aalto University January 26, 2012 Contents 1 Batch and Recursive Estimation
More informationELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS
EC 32 (CR) Total No. of Questions :09] [Total No. of Pages : 02 III/IV B.Tech. DEGREE EXAMINATIONS, APRIL/MAY- 207 Second Semester ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS Time: Three Hours
More informationCoherentDetectionof OFDM
Telematics Lab IITK p. 1/50 CoherentDetectionof OFDM Indo-UK Advanced Technology Centre Supported by DST-EPSRC K Vasudevan Associate Professor vasu@iitk.ac.in Telematics Lab Department of EE Indian Institute
More informationWireless Communications Lecture 10
Wireless Communications Lecture 1 [SNR per symbol and SNR per bit] SNR = P R N B = E s N BT s = E b N BT b For BPSK: T b = T s, E b = E s, and T s = 1/B. Raised cosine pulse shaper for other pulses. T
More informationExpectation propagation for signal detection in flat-fading channels
Expectation propagation for signal detection in flat-fading channels Yuan Qi MIT Media Lab Cambridge, MA, 02139 USA yuanqi@media.mit.edu Thomas Minka CMU Statistics Department Pittsburgh, PA 15213 USA
More informationPreliminary Studies on DFE Error Propagation, Precoding, and their Impact on KP4 FEC Performance for PAM4 Signaling Systems
Preliminary Studies on DFE Error Propagation, Precoding, and their Impact on KP4 FEC Performance for PAM4 Signaling Systems Geoff Zhang September, 2018 Outline 1/(1+D) precoding for PAM4 link systems 1/(1+D)
More informationMapper & De-Mapper System Document
Mapper & De-Mapper System Document Mapper / De-Mapper Table of Contents. High Level System and Function Block. Mapper description 2. Demodulator Function block 2. Decoder block 2.. De-Mapper 2..2 Implementation
More informationConstellation Shaping for Communication Channels with Quantized Outputs
Constellation Shaping for Communication Channels with Quantized Outputs Chandana Nannapaneni, Matthew C. Valenti, and Xingyu Xiang Lane Department of Computer Science and Electrical Engineering West Virginia
More informationOne Lesson of Information Theory
Institut für One Lesson of Information Theory Prof. Dr.-Ing. Volker Kühn Institute of Communications Engineering University of Rostock, Germany Email: volker.kuehn@uni-rostock.de http://www.int.uni-rostock.de/
More informationPartially Observable Markov Decision Processes (POMDPs) Pieter Abbeel UC Berkeley EECS
Partially Observable Markov Decision Processes (POMDPs) Pieter Abbeel UC Berkeley EECS Many slides adapted from Jur van den Berg Outline POMDPs Separation Principle / Certainty Equivalence Locally Optimal
More information16.36 Communication Systems Engineering
MIT OpenCourseWare http://ocw.mit.edu 16.36 Communication Systems Engineering Spring 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 16.36: Communication
More informationMobile Communications (KECE425) Lecture Note Prof. Young-Chai Ko
Mobile Communications (KECE425) Lecture Note 20 5-19-2014 Prof Young-Chai Ko Summary Complexity issues of diversity systems ADC and Nyquist sampling theorem Transmit diversity Channel is known at the transmitter
More informationBlind Equalization via Particle Filtering
Blind Equalization via Particle Filtering Yuki Yoshida, Kazunori Hayashi, Hideaki Sakai Department of System Science, Graduate School of Informatics, Kyoto University Historical Remarks A sequential Monte
More informationINTRODUCTION TO DELTA-SIGMA ADCS
ECE37 Advanced Analog Circuits INTRODUCTION TO DELTA-SIGMA ADCS Richard Schreier richard.schreier@analog.com NLCOTD: Level Translator VDD > VDD2, e.g. 3-V logic? -V logic VDD < VDD2, e.g. -V logic? 3-V
More informationSlide Set Data Converters. Digital Enhancement Techniques
0 Slide Set Data Converters Digital Enhancement Techniques Introduction Summary Error Measurement Trimming of Elements Foreground Calibration Background Calibration Dynamic Matching Decimation and Interpolation
More informationRevision of Lecture 4
Revision of Lecture 4 We have discussed all basic components of MODEM Pulse shaping Tx/Rx filter pair Modulator/demodulator Bits map symbols Discussions assume ideal channel, and for dispersive channel
More informationEE 230 Lecture 40. Data Converters. Amplitude Quantization. Quantization Noise
EE 230 Lecture 40 Data Converters Amplitude Quantization Quantization Noise Review from Last Time: Time Quantization Typical ADC Environment Review from Last Time: Time Quantization Analog Signal Reconstruction
More informationParticle Filter for Joint Blind Carrier Frequency Offset Estimation and Data Detection
Particle Filter for Joint Blind Carrier Frequency Offset Estimation and Data Detection Ali A. Nasir, Salman Durrani and Rodney A. Kennedy School of Engineering, CECS, The Australian National University,
More informationECE 564/645 - Digital Communications, Spring 2018 Homework #2 Due: March 19 (In Lecture)
ECE 564/645 - Digital Communications, Spring 018 Homework # Due: March 19 (In Lecture) 1. Consider a binary communication system over a 1-dimensional vector channel where message m 1 is sent by signaling
More informationDirect-Sequence Spread-Spectrum
Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.
More informationANALYSIS OF A PARTIAL DECORRELATOR IN A MULTI-CELL DS/CDMA SYSTEM
ANAYSIS OF A PARTIA DECORREATOR IN A MUTI-CE DS/CDMA SYSTEM Mohammad Saquib ECE Department, SU Baton Rouge, A 70803-590 e-mail: saquib@winlab.rutgers.edu Roy Yates WINAB, Rutgers University Piscataway
More informationOn the Low-SNR Capacity of Phase-Shift Keying with Hard-Decision Detection
On the Low-SNR Capacity of Phase-Shift Keying with Hard-Decision Detection ustafa Cenk Gursoy Department of Electrical Engineering University of Nebraska-Lincoln, Lincoln, NE 68588 Email: gursoy@engr.unl.edu
More informationLecture 18: Gaussian Channel
Lecture 18: Gaussian Channel Gaussian channel Gaussian channel capacity Dr. Yao Xie, ECE587, Information Theory, Duke University Mona Lisa in AWGN Mona Lisa Noisy Mona Lisa 100 100 200 200 300 300 400
More informationLecture 2: From Linear Regression to Kalman Filter and Beyond
Lecture 2: From Linear Regression to Kalman Filter and Beyond January 18, 2017 Contents 1 Batch and Recursive Estimation 2 Towards Bayesian Filtering 3 Kalman Filter and Bayesian Filtering and Smoothing
More informationEE 5345 Biomedical Instrumentation Lecture 12: slides
EE 5345 Biomedical Instrumentation Lecture 1: slides 4-6 Carlos E. Davila, Electrical Engineering Dept. Southern Methodist University slides can be viewed at: http:// www.seas.smu.edu/~cd/ee5345.html EE
More informationPosterior Cramer-Rao Lower Bound for Mobile Tracking in Mixed Line-of-Sight/Non Line-of-Sight Conditions
Posterior Cramer-Rao Lower Bound for Mobile Tracking in Mixed Line-of-Sight/Non Line-of-Sight Conditions Chen Liang 1,2, Wu Lenan 2, Robert Piché 1 1 Tampere University of Technology, Finland 2 Southeast
More informationSupplementary Figure 1: Scheme of the RFT. (a) At first, we separate two quadratures of the field (denoted by and ); (b) then, each quadrature
Supplementary Figure 1: Scheme of the RFT. (a At first, we separate two quadratures of the field (denoted by and ; (b then, each quadrature undergoes a nonlinear transformation, which results in the sine
More informationMODULATION AND CODING FOR QUANTIZED CHANNELS. Xiaoying Shao and Harm S. Cronie
MODULATION AND CODING FOR QUANTIZED CHANNELS Xiaoying Shao and Harm S. Cronie x.shao@ewi.utwente.nl, h.s.cronie@ewi.utwente.nl University of Twente, Faculty of EEMCS, Signals and Systems Group P.O. box
More informationComputation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems
Computation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems Department of Electrical Engineering, College of Engineering, Basrah University Basrah Iraq,
More informationIssues with sampling time and jitter in Annex 93A. Adam Healey IEEE P802.3bj Task Force May 2013
Issues with sampling time and jitter in Annex 93A Adam Healey IEEE P802.3bj Task Force May 2013 Part 1: Jitter (comment #157) 2 Treatment of jitter in COM Draft 2.0 h (0) (t s ) slope h(0) (t s ) 1 UI
More informationA Nonuniform Quantization Scheme for High Speed SAR ADC Architecture
A Nonuniform Quantization Scheme for High Speed SAR ADC Architecture Youngchun Kim Electrical and Computer Engineering The University of Texas Wenjuan Guo Intel Corporation Ahmed H Tewfik Electrical and
More informationCoding theory: Applications
INF 244 a) Textbook: Lin and Costello b) Lectures (Tu+Th 12.15-14) covering roughly Chapters 1,9-12, and 14-18 c) Weekly exercises: For your convenience d) Mandatory problem: Programming project (counts
More informationELEC546 Review of Information Theory
ELEC546 Review of Information Theory Vincent Lau 1/1/004 1 Review of Information Theory Entropy: Measure of uncertainty of a random variable X. The entropy of X, H(X), is given by: If X is a discrete random
More informationCSEP 573: Artificial Intelligence
CSEP 573: Artificial Intelligence Hidden Markov Models Luke Zettlemoyer Many slides over the course adapted from either Dan Klein, Stuart Russell, Andrew Moore, Ali Farhadi, or Dan Weld 1 Outline Probabilistic
More informationElectrical Engineering Written PhD Qualifier Exam Spring 2014
Electrical Engineering Written PhD Qualifier Exam Spring 2014 Friday, February 7 th 2014 Please do not write your name on this page or any other page you submit with your work. Instead use the student
More informationTowards control over fading channels
Towards control over fading channels Paolo Minero, Massimo Franceschetti Advanced Network Science University of California San Diego, CA, USA mail: {minero,massimo}@ucsd.edu Invited Paper) Subhrakanti
More informationDetection and Estimation Theory
Detection and Estimation Theory Instructor: Prof. Namrata Vaswani Dept. of Electrical and Computer Engineering Iowa State University http://www.ece.iastate.edu/ namrata Slide 1 What is Estimation and Detection
More informationOversampling Converters
Oversampling Converters David Johns and Ken Martin (johns@eecg.toronto.edu) (martin@eecg.toronto.edu) slide 1 of 56 Motivation Popular approach for medium-to-low speed A/D and D/A applications requiring
More informationIntroduction p. 1 Fundamental Problems p. 2 Core of Fundamental Theory and General Mathematical Ideas p. 3 Classical Statistical Decision p.
Preface p. xiii Acknowledgment p. xix Introduction p. 1 Fundamental Problems p. 2 Core of Fundamental Theory and General Mathematical Ideas p. 3 Classical Statistical Decision p. 4 Bayes Decision p. 5
More informationAn Anti-Aliasing Multi-Rate Σ Modulator
An Anti-Aliasing Multi-Rate Σ Modulator Anthony Chan Carusone Depart. of Elec. and Comp. Eng. University of Toronto, Canada Franco Maloberti Department of Electronics University of Pavia, Italy May 6,
More informationExploiting Sparsity for Wireless Communications
Exploiting Sparsity for Wireless Communications Georgios B. Giannakis Dept. of ECE, Univ. of Minnesota http://spincom.ece.umn.edu Acknowledgements: D. Angelosante, J.-A. Bazerque, H. Zhu; and NSF grants
More informationNovel spectrum sensing schemes for Cognitive Radio Networks
Novel spectrum sensing schemes for Cognitive Radio Networks Cantabria University Santander, May, 2015 Supélec, SCEE Rennes, France 1 The Advanced Signal Processing Group http://gtas.unican.es The Advanced
More informationTurbo Codes for xdsl modems
Turbo Codes for xdsl modems Juan Alberto Torres, Ph. D. VOCAL Technologies, Ltd. (http://www.vocal.com) John James Audubon Parkway Buffalo, NY 14228, USA Phone: +1 716 688 4675 Fax: +1 716 639 0713 Email:
More informationChannel capacity. Outline : 1. Source entropy 2. Discrete memoryless channel 3. Mutual information 4. Channel capacity 5.
Channel capacity Outline : 1. Source entropy 2. Discrete memoryless channel 3. Mutual information 4. Channel capacity 5. Exercices Exercise session 11 : Channel capacity 1 1. Source entropy Given X a memoryless
More informationMarkov localization uses an explicit, discrete representation for the probability of all position in the state space.
Markov Kalman Filter Localization Markov localization localization starting from any unknown position recovers from ambiguous situation. However, to update the probability of all positions within the whole
More informationFBMC/OQAM transceivers for 5G mobile communication systems. François Rottenberg
FBMC/OQAM transceivers for 5G mobile communication systems François Rottenberg Modulation Wikipedia definition: Process of varying one or more properties of a periodic waveform, called the carrier signal,
More informationECE 564/645 - Digital Communications, Spring 2018 Midterm Exam #1 March 22nd, 7:00-9:00pm Marston 220
ECE 564/645 - Digital Communications, Spring 08 Midterm Exam # March nd, 7:00-9:00pm Marston 0 Overview The exam consists of four problems for 0 points (ECE 564) or 5 points (ECE 645). The points for each
More informationSigma-Delta modulation based distributed detection in wireless sensor networks
Louisiana State University LSU Digital Commons LSU Master's Theses Graduate School 2007 Sigma-Delta modulation based distributed detection in wireless sensor networks Dimeng Wang Louisiana State University
More informationMultimedia Networking ECE 599
Multimedia Networking ECE 599 Prof. Thinh Nguyen School of Electrical Engineering and Computer Science Based on lectures from B. Lee, B. Girod, and A. Mukherjee 1 Outline Digital Signal Representation
More informationBurst Markers in EPoC Syed Rahman, Huawei Nicola Varanese, Qualcomm
Burst Markers in EPoC Syed Rahman, Huawei Nicola Varanese, Qualcomm Page 1 Introduction Burst markers are used to indicate the start and end of each burst in EPoC burst mode The same marker delimits the
More informationADAPTIVE EQUALIZATION AT MULTI-GHZ DATARATES
ADAPTIVE EQUALIZATION AT MULTI-GHZ DATARATES Department of Electrical Engineering Indian Institute of Technology, Madras 1st February 2007 Outline Introduction. Approaches to electronic mitigation - ADC
More informationJOINT ITERATIVE DETECTION AND DECODING IN THE PRESENCE OF PHASE NOISE AND FREQUENCY OFFSET
JOINT ITERATIVE DETECTION AND DECODING IN THE PRESENCE OF PHASE NOISE AND FREQUENCY OFFSET Alan Barbieri, Giulio Colavolpe and Giuseppe Caire Università di Parma Institut Eurecom Dipartimento di Ingegneria
More informationPhysical Layer and Coding
Physical Layer and Coding Muriel Médard Professor EECS Overview A variety of physical media: copper, free space, optical fiber Unified way of addressing signals at the input and the output of these media:
More informationMulticarrier transmission DMT/OFDM
W. Henkel, International University Bremen 1 Multicarrier transmission DMT/OFDM DMT: Discrete Multitone (wireline, baseband) OFDM: Orthogonal Frequency Division Multiplex (wireless, with carrier, passband)
More informationGaussian channel. Information theory 2013, lecture 6. Jens Sjölund. 8 May Jens Sjölund (IMT, LiU) Gaussian channel 1 / 26
Gaussian channel Information theory 2013, lecture 6 Jens Sjölund 8 May 2013 Jens Sjölund (IMT, LiU) Gaussian channel 1 / 26 Outline 1 Definitions 2 The coding theorem for Gaussian channel 3 Bandlimited
More informationRANDOM DISCRETE MEASURE OF THE PHASE POSTERIOR PDF IN TURBO SYNCHRONIZATION
RANDOM DISCRETE MEASURE OF THE PHASE POSTERIOR PDF IN TURBO SYNCHRONIZATION Nicolas Paul, Didier Le Ruyet, Tanya Bertozzi * Michel Terre Electronique et Communications, CNAM, 292 rue Saint Martin, 75141
More informationa) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics.
Digital Modulation and Coding Tutorial-1 1. Consider the signal set shown below in Fig.1 a) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics. b) What is the minimum Euclidean
More informationMultiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks. Ji an Luo
Multiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks Ji an Luo 2008.6.6 Outline Background Problem Statement Main Results Simulation Study Conclusion Background Wireless
More informationEE 521: Instrumentation and Measurements
Aly El-Osery Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA September 23, 2009 1 / 18 1 Sampling 2 Quantization 3 Digital-to-Analog Converter 4 Analog-to-Digital Converter
More informationDecision Weighted Adaptive Algorithms with Applications to Wireless Channel Estimation
Decision Weighted Adaptive Algorithms with Applications to Wireless Channel Estimation Shane Martin Haas April 12, 1999 Thesis Defense for the Degree of Master of Science in Electrical Engineering Department
More informationPerformance of Multi Binary Turbo-Codes on Nakagami Flat Fading Channels
Buletinul Ştiinţific al Universităţii "Politehnica" din Timişoara Seria ELECTRONICĂ şi TELECOMUNICAŢII TRANSACTIONS on ELECTRONICS and COMMUNICATIONS Tom 5(65), Fascicola -2, 26 Performance of Multi Binary
More informationAdvanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 19 Multi-User CDMA Uplink and Asynchronous CDMA
More informationEE 661: Modulation Theory Solutions to Homework 6
EE 66: Modulation Theory Solutions to Homework 6. Solution to problem. a) Binary PAM: Since 2W = 4 KHz and β = 0.5, the minimum T is the solution to (+β)/(2t ) = W = 2 0 3 Hz. Thus, we have the maximum
More informationTitle. Author(s)Tsai, Shang-Ho. Issue Date Doc URL. Type. Note. File Information. Equal Gain Beamforming in Rayleigh Fading Channels
Title Equal Gain Beamforming in Rayleigh Fading Channels Author(s)Tsai, Shang-Ho Proceedings : APSIPA ASC 29 : Asia-Pacific Signal Citationand Conference: 688-691 Issue Date 29-1-4 Doc URL http://hdl.handle.net/2115/39789
More informationA Hilbert Space for Random Processes
Gaussian Basics Random Processes Filtering of Random Processes Signal Space Concepts A Hilbert Space for Random Processes I A vector space for random processes X t that is analogous to L 2 (a, b) is of
More informationOutput MAI Distributions of Linear MMSE Multiuser Receivers in DS-CDMA Systems
1128 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 3, MARCH 2001 Output MAI Distributions of Linear MMSE Multiuser Receivers in DS-CDMA Systems Junshan Zhang, Member, IEEE, Edwin K. P. Chong, Senior
More informationWideband Spectrum Sensing for Cognitive Radios
Wideband Spectrum Sensing for Cognitive Radios Zhi (Gerry) Tian ECE Department Michigan Tech University ztian@mtu.edu February 18, 2011 Spectrum Scarcity Problem Scarcity vs. Underutilization Dilemma US
More informationAdaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems
ACSTSK Adaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems Professor Sheng Chen Electronics and Computer Science University of Southampton Southampton SO7 BJ, UK E-mail: sqc@ecs.soton.ac.uk
More informationImproved Detected Data Processing for Decision-Directed Tracking of MIMO Channels
Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Emna Eitel and Joachim Speidel Institute of Telecommunications, University of Stuttgart, Germany Abstract This paper addresses
More informationNonlinearity Equalization Techniques for DML- Transmission Impairments
Nonlinearity Equalization Techniques for DML- Transmission Impairments Johannes von Hoyningen-Huene jhh@tf.uni-kiel.de Christian-Albrechts-Universität zu Kiel Workshop on Optical Communication Systems
More informationOn the Performance of SC-MMSE-FD Equalization for Fixed-Point Implementations
On the Performance of SC-MMSE-FD Equalization for Fixed-Point Implementations ISTC 2014, Bremen, Tamal Bose and Friedrich K. Jondral KIT Universität des Landes Baden-Württemberg und nationales Forschungszentrum
More informationThe PPM Poisson Channel: Finite-Length Bounds and Code Design
August 21, 2014 The PPM Poisson Channel: Finite-Length Bounds and Code Design Flavio Zabini DEI - University of Bologna and Institute for Communications and Navigation German Aerospace Center (DLR) Balazs
More informationSEQUENTIAL MONTE CARLO METHODS WITH APPLICATIONS TO COMMUNICATION CHANNELS. A Thesis SIRISH BODDIKURAPATI
SEQUENTIAL MONTE CARLO METHODS WITH APPLICATIONS TO COMMUNICATION CHANNELS A Thesis by SIRISH BODDIKURAPATI Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of
More informationSemi-Blind ML Synchronization for UWB Systems
Semi-Blind ML Synchronization for UWB Systems Stefan Franz, Cecilia Carbonelli, Urbashi Mitra Communication Sciences Institute, University of Southern California 3740 McClintock Avenue, Los Angeles, CA,
More informationPartially Observable Markov Decision Processes (POMDPs)
Partially Observable Markov Decision Processes (POMDPs) Sachin Patil Guest Lecture: CS287 Advanced Robotics Slides adapted from Pieter Abbeel, Alex Lee Outline Introduction to POMDPs Locally Optimal Solutions
More informationChapter 4: Continuous channel and its capacity
meghdadi@ensil.unilim.fr Reference : Elements of Information Theory by Cover and Thomas Continuous random variable Gaussian multivariate random variable AWGN Band limited channel Parallel channels Flat
More informationInteractions of Information Theory and Estimation in Single- and Multi-user Communications
Interactions of Information Theory and Estimation in Single- and Multi-user Communications Dongning Guo Department of Electrical Engineering Princeton University March 8, 2004 p 1 Dongning Guo Communications
More informationStreaming Variational Bayes
Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan UC Berkeley Discussion led by Miao Liu September 13, 2013 Introduction The SDA-Bayes Framework
More informationWireless Information Transmission System Lab. Channel Estimation. Institute of Communications Engineering. National Sun Yat-sen University
Wireless Information Transmission System Lab. Channel Estimation Institute of Communications Engineering National Sun Yat-sen University Table of Contents Introduction to Channel Estimation Generic Pilot
More informationModulation Classification and Parameter Estimation in Wireless Networks
Syracuse University SURFACE Electrical Engineering - Theses College of Engineering and Computer Science 6-202 Modulation Classification and Parameter Estimation in Wireless Networks Ruoyu Li Syracuse University,
More informationEs e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0
Problem 6.15 : he received signal-plus-noise vector at the output of the matched filter may be represented as (see (5-2-63) for example) : r n = E s e j(θn φ) + N n where θ n =0,π/2,π,3π/2 for QPSK, and
More informationA Design of High-Rate Space-Frequency Codes for MIMO-OFDM Systems
A Design of High-Rate Space-Frequency Codes for MIMO-OFDM Systems Wei Zhang, Xiang-Gen Xia and P. C. Ching xxia@ee.udel.edu EE Dept., The Chinese University of Hong Kong ECE Dept., University of Delaware
More information2D Image Processing. Bayes filter implementation: Kalman filter
2D Image Processing Bayes filter implementation: Kalman filter Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de
More information